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公开(公告)号：US20240354580A1
公开(公告)日：2024-10-24
申请号：US18758605
申请日：2024-06-28
Inventor： Zhipeng Liang , Weimin Zhou , Yi Li , Zonghong Dai
IPC: G06N3/086 , G06N3/0475
CPC classification number: G06N3/086 , G06N3/0475
Abstract: A neural network architecture search method includes: receiving an optimization request, where the optimization request includes a model file and an optimization requirement of a to-be-optimized model, and the optimization requirement includes a performance requirement and a hardware requirement; performing neural architecture search processing in search space based on the model file, to obtain a neural network architecture that meets the optimization requirement; and returning the neural network architecture.
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公开(公告)号：US20240095529A1
公开(公告)日：2024-03-21
申请号：US18521152
申请日：2023-11-28
Inventor： Weimin Zhou , Yuting Mai , Yi Li , Yijun Guo , Binbin Deng , Zonghong Dai
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: A neural network optimization method includes receiving a model file of a to-be-optimized neural network; obtaining a search space of a target neural network architecture based on the model file of the to-be-optimized neural network, where the search space includes a value range of each attribute of each neuron in the target neural network architecture; obtaining the target neural network architecture based on the search space; training the target neural network architecture based on the model file of the to-be-optimized neural network, to obtain a model file of a target neural network; and providing the model file of the target neural network to a user.
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公开(公告)号：US20240281641A1
公开(公告)日：2024-08-22
申请号：US18653096
申请日：2024-05-02
Inventor： Weimin Zhou , Yifeng Liu , Yi Li , Zonghong Dai
IPC: G06N3/045
CPC classification number: G06N3/045
Abstract: A model weight obtaining method includes obtaining structure information of a first neural network model; searching, based on the structure information of the first neural network model, a weight library that stores a plurality of groups of historical weights to obtain a reference weight, where the reference weight is a weight of a second neural network model having a structure similar to that of the first neural network model in the plurality of groups of historical weights; and converting the reference weight to obtain a weight of the first neural network model. In the method, a weight of a neural network model having a structure similar to that of a to-be-trained neural network model is searched for in a weight library, and the weight is converted, to obtain a weight that can be used by the to-be-trained neural network model.
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公开(公告)号：US20230206132A1
公开(公告)日：2023-06-29
申请号：US18179661
申请日：2023-03-07
Inventor： Jiangcheng Zhu , Zhesi Huang , Renke Wu , Xiaolong Bai , Bingbing Yang , Yi Li , Jinghua Zhong , Zonghong Dai
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A method is applied to an artificial intelligence (AI) platform and includes providing a training configuration interface for a user, where the training configuration interface includes a plurality of training modes for the user to select, and each training mode represents an allocation policy for compute nodes required for training an initial AI model; generating at least one training task based on a selection of the user on the training configuration interface; and performing the at least one training task to train the initial AI model, to obtain an AI model, where the obtained AI model is provided for the user to download or use.
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公开(公告)号：US20230334325A1
公开(公告)日：2023-10-19
申请号：US18339025
申请日：2023-06-21
Inventor： Yi Li , Yunwen Lian
IPC: G06N3/084
CPC classification number: G06N3/084
Abstract: An index table may be dynamically adjusted based on the gradient information in a training process, and further, the corresponding second training data subset may be read based on the index table in the next round. The training data is evaluated in each round, and a training data set in the training process is dynamically adjusted.
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公开(公告)号：US20220414426A1
公开(公告)日：2022-12-29
申请号：US17902206
申请日：2022-09-02
Inventor： Weimin Zhou , Yijun Guo , Yi Li , Yuting Mai , Binbin Deng
Abstract: This application provides a neural architecture search method, applied to a search system. The search system includes a generator and a searcher. The method includes: The generator generates a plurality of neural network architectures based on a search space; the searcher obtains evaluation indicator values of a plurality of child models obtained based on the plurality of neural network architectures on first hardware; and the searcher determines, based on the neural network architectures corresponding to the plurality of child models and the evaluation indicator values of the plurality of child models on the first hardware, a first target neural network architecture that meets a preset condition. In this way, different initial child model training processes are decoupled, and a neural architecture search process is decoupled from an initial child model training process, so that search duration is reduced and search efficiency is improved.
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公开(公告)号：US20220207397A1
公开(公告)日：2022-06-30
申请号：US17696040
申请日：2022-03-16
Inventor： Yi Chen , Pengfei Li , Yi Li , Xiaolong Bai
Abstract: An AI model evaluation method includes: obtaining an AI model and an evaluation data set, where the evaluation data set includes a plurality of pieces of evaluation data carrying labels that are used to indicate real results corresponding to the evaluation data; classifying the evaluation data in the evaluation data set based on a data feature to obtain an evaluation data subset; and calculating inference accuracy of the AI model on the evaluation data subset to obtain an evaluation result of the AI model on data whose value of the data feature meets the condition.
